ishrak-zarif Goto Github PK

followers: 13.0 following: 21.0 repos: 29.0 gists: 0.0

Name: Ishrak Zarif

Type: User

Location: Milwaukee, USA.

Hi there 👋

🔭 I’m currently working on robotics, IoT, AR/VR.
🌱 I’m currently learning AR/VR.

Ishrak Zarif's Projects

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

ai-expert-roadmap

Roadmap to becoming an Artificial Intelligence Expert in 2022

analysis-of-vaccination-effect-on-covid-19-cases-all-over-the-world

Advanced Machine Learning Course Project

attack-and-anomaly-detection-in-iot-sensors-in-iot-sites-using-machine-learning-approaches

Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.

awesome-leetcode-resources

This repository contains LeetCode articles which are very useful during the interview preparation.

awesome-public-datasets

A topic-centric list of HQ open datasets. PR ☛☛☛

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-r-learning-resources

A curated collection of free resources to help deepen your understanding of the R programming language. Updated regularly. Contributions encouraged via pull request (see contributing.md).